Quantitative understanding of success and inequality through network science

The unprecedented availability of large-scale datasets on human activities and interactions has enabled us to quantitatively understand how networks shape success and drive inequalities in various fields. In this talk, I will highlight the crucial role of network science in comprehending social phenomena, particularly in the realms of art and science. Specifically, I will present a series of findings on the evolution of careers in the arts, utilizing a random walk model that successfully predicts artists’ success even twenty years into the future. I will then shift my focus to the COVID-19 pandemic and the advent of Large Language models, discussing how they have amplified network inequalities in science. Through these examples, I will demonstrate the importance of network science in measuring, predicting, and designing algorithms for social phenomena.